A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks
This work addresses the need for precise spectrum demand characterization to inform policy-making in wireless connectivity, representing a domain-specific incremental improvement.
The paper tackled the problem of predicting spectrum demand for efficient spectrum management by introducing HR-GAT, a hierarchical resolution graph attention network model, which improved predictive accuracy by 21% over eight baseline models across five major Canadian cities.
The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.